Keywords: auto-encoder, generative models, GAN, VAE, unsupervised learning
TL;DR: We propose a new auto-encoder based on the Wasserstein distance, which improves on the sampling properties of VAE.
Abstract: We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-Encoder (VAE). This regularizer encourages the encoded training distribution to match the prior. We compare our algorithm with several other techniques and show that it is a generalization of adversarial auto-encoders (AAE). Our experiments show that WAE shares many of the properties of VAEs (stable training, encoder-decoder architecture, nice latent manifold structure) while generating samples of better quality.
Code: [![github](/images/github_icon.svg) tolstikhin/wae](https://github.com/tolstikhin/wae) + [![Papers with Code](/images/pwc_icon.svg) 11 community implementations](https://paperswithcode.com/paper/?openreview=HkL7n1-0b)
Data: [CelebA](https://paperswithcode.com/dataset/celeba), [MNIST](https://paperswithcode.com/dataset/mnist)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 9 code implementations](https://www.catalyzex.com/paper/arxiv:1711.01558/code)